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Soft Error Resilience of Deep Residual Networks for Object Recognition
Convolutional Neural Networks (CNNs) have truly gained attention in object recognition and object classification in particular. When being implemented on Graphics Processing Units (GPUs), deeper networks are more accurate than shallow ones. Residual Networks (ResNets) are one of the deepest CNN architectures used in various fields including safety-critical ones. GPUs have proven to be the major accelerator for CNN models. However, modern GPUs are prone to radiation-induced soft errors, which isdoi:10.1109/access.2020.2968129 fatcat:qsni4ga5ojbydo36nnicw2b6b4